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A Hybrid Approach for Improving Prediction Coverage of Collaborative Filtering

  • Manolis G. Vozalis
  • Angelos I. Markos
  • Konstantinos G. Margaritis
Conference paper
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)

Abstract

In this paper we present a hybrid filtering algorithm that attempts to deal with low prediction Coverage, a problem especially present in sparse datasets. We focus on Item HyCoV, an implementation of the proposed approach that incorporates an additional User-based step to the base Item-based algorithm, in order to take into account the possible contribution of users similar to the active user. A series of experiments were executed, aiming to evaluate the proposed approach in terms of Coverage and Accuracy. The results show that Item HyCov significantly improves both performance measures, requiring no additional data and minimal modification of existing filtering systems.

Keywords

Active User Recommender System Collaborative Filter Mean Absolute Error Common Item 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Manolis G. Vozalis
    • 1
  • Angelos I. Markos
    • 1
  • Konstantinos G. Margaritis
    • 1
  1. 1.Department of Applied InformaticsUniversity of MacedoniaThessalonikiGreece

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